CN117546189A - Processing apparatus and processing method - Google Patents
Processing apparatus and processing method Download PDFInfo
- Publication number
- CN117546189A CN117546189A CN202180099392.XA CN202180099392A CN117546189A CN 117546189 A CN117546189 A CN 117546189A CN 202180099392 A CN202180099392 A CN 202180099392A CN 117546189 A CN117546189 A CN 117546189A
- Authority
- CN
- China
- Prior art keywords
- time
- building
- history information
- group
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 title claims abstract description 96
- 238000003672 processing method Methods 0.000 title claims description 10
- 230000004044 response Effects 0.000 claims abstract description 75
- 238000000034 method Methods 0.000 claims abstract description 49
- 238000012423 maintenance Methods 0.000 claims abstract description 48
- 230000008569 process Effects 0.000 claims abstract description 24
- 238000004364 calculation method Methods 0.000 claims description 32
- 230000010485 coping Effects 0.000 claims description 14
- 238000012790 confirmation Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 16
- 238000003860 storage Methods 0.000 description 11
- 238000004458 analytical method Methods 0.000 description 9
- 238000002360 preparation method Methods 0.000 description 9
- 230000000694 effects Effects 0.000 description 6
- 238000009826 distribution Methods 0.000 description 3
- 230000002950 deficient Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000000585 Mann–Whitney U test Methods 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000010813 municipal solid waste Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000012353 t test Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The processing device (100) processes information relating to maintenance of building equipment (10) provided in a building (1). The processing device (100) is provided with an acquisition unit (130), a classification unit (131), and an output unit (133). An acquisition unit (130) acquires a plurality of pieces of history information. A classification unit (131) classifies a plurality of pieces of history information into a plurality of groups by a clustering method. An output unit (133) outputs the classification result of the classification unit (131). The plurality of history information includes a response time from when a maintenance person arrives at the building (1) to when a job relating to the building equipment (10) is completed. The plurality of groups includes a 1 st group including no non-job time and a 2 nd group including no job time. A classification unit (131) classifies a plurality of history information into at least a 1 st group and a 2 nd group based on the handling time.
Description
Technical Field
The present disclosure relates to a processing apparatus and a processing method.
Background
With respect to maintenance of building equipment installed in a building, an operation is performed in which a history of maintenance work is stored as history information. The history information includes a response time from when a maintenance person arrives at the building to when a job related to the building equipment is completed.
Conventionally, a method of predicting the response time based on the accumulated history information has been proposed. For example, there is a method of taking an average value of the past coping time as a predicted value of the coping time. As a technique for estimating the working time, there is a technique described in, for example, japanese patent application laid-open No. 2017-151490 (patent document 1).
Prior art literature
Patent literature
Patent document 1: japanese patent application laid-open No. 2017-151490
Disclosure of Invention
Problems to be solved by the invention
However, the response time may include not only the work time but also a non-work time such as a time until the building is entered and a time when the work is interrupted to prepare a part necessary for the work. When the non-working time having such a property different from the actual working time is included, it is difficult to predict the response time from only the average value. In the technique described in patent document 1, no study has been made on a method of predicting the handling time in consideration of the case where the handling time includes time having different properties.
The present disclosure has been made to solve such a problem, and an object thereof is to provide a processing apparatus and a processing method capable of appropriately performing analysis related to a handling time even when different times having different properties such as a non-working time are included.
Means for solving the problems
The processing device of the present disclosure processes information related to maintenance of building equipment provided in a building. The processing device includes an acquisition unit, a classification unit, and an output unit. The acquisition unit acquires a plurality of pieces of history information. The classification section classifies the plurality of history information into a plurality of groups by a clustering method. The output unit outputs the classification result of the classification unit. The plurality of history information includes a response time from when a maintenance person arrives at the building to when a job related to the building equipment is completed. The plurality of groups includes a 1 st group including no non-job time and a 2 nd group including no job time. The classifying unit classifies the plurality of history information into at least 1 st and 2 nd groups based on the coping time.
The processing method of the present disclosure is a method of processing information related to maintenance of building equipment provided in a building. The processing method comprises the following steps: acquiring a plurality of history information; classifying the plurality of history information into a plurality of groups by a clustering method; and outputting the classification result of the step of classifying. The plurality of history information includes a response time from when a maintenance person arrives at the building to when a job related to the building equipment is completed. The plurality of groups includes a 1 st group including no non-job time and a 2 nd group including no job time. In the classifying step, the plurality of history information is classified into at least the 1 st group and the 2 nd group based on the coping time.
ADVANTAGEOUS EFFECTS OF INVENTION
According to the present disclosure, even when the time having different properties such as the non-working time is included, the analysis related to the handling time can be appropriately performed.
Drawings
Fig. 1 is a diagram showing an example of a functional block diagram of the processing device according to embodiment 1.
Fig. 2 is a diagram showing an example of a hardware configuration of the processing apparatus according to embodiment 1.
Fig. 3 is a graph for explaining distribution of response times.
Fig. 4 is a flowchart of a process executed by the processing device according to embodiment 1.
Fig. 5 is a diagram showing a display example of the classification result of embodiment 1.
Fig. 6 is a diagram showing a display example of the calculation result of embodiment 1.
Fig. 7 is a diagram showing a display example of the calculation result of embodiment 1.
Fig. 8 is a diagram showing a display example of the calculation result of embodiment 1.
Fig. 9 is a diagram showing an example of a functional block diagram of the processing device according to embodiment 2.
Fig. 10 is a flowchart of a process performed by the processing device according to embodiment 2.
Detailed Description
Hereinafter, embodiments will be described with reference to the drawings. In the following description, the same reference numerals are given to the same components. Their names and functions are also identical. Therefore, detailed descriptions thereof will not be repeated.
[ embodiment 1 ]
First, the processing apparatus 100 according to embodiment 1 will be described. Fig. 1 is a diagram showing an example of a functional block diagram of a processing apparatus 100 according to embodiment 1. Fig. 2 is a diagram showing an example of a hardware configuration of the processing apparatus 100 according to embodiment 1.
The processing device 100 in embodiment 1 is a device that processes information related to maintenance of building equipment installed in a building. Specifically, the processing device 100 classifies a plurality of job history information (also referred to as "history information") stored in the storage unit 114, calculates a statistical value based on the classified result, and displays the classification result or the calculation result on the display device 201.
The history information is information recorded as history when a job is performed in response to a query, a complaint, or the like. As shown in fig. 2, the history information is recorded with information including a plurality of buildings 1a to 1 c.
The plurality of history information includes a response time, respectively. The response time is a time from when a maintenance person arrives at the building (1 a to 1c, etc.) to when the job relating to the building equipment (10 a to 10c, etc.) is completed. For example, when a maintenance operation is performed in the building 1a, the time from when a maintenance person arrives at the building 1a until the operation related to the building equipment 10a is completed becomes a handling time.
The plurality of history information includes information such as a work date and time, a type (model) of the elevator, and a type (failure type) of the elevator failure, in addition to the response time.
The history information and the response time will be described in detail with reference to fig. 3 to 8. In the following description, the buildings 1a to 1c and the like are collectively referred to as "building 1", and the building devices 10a to 10c and the like are collectively referred to as "building device 10". Here, as the building equipment 10, an elevator such as an elevator is assumed, but other building equipment may be used.
As shown in fig. 1, the processing apparatus 100 includes a storage unit 114, an acquisition unit 130, a classification unit 131, a calculation unit 132, and an output unit 133. The acquisition unit 130 acquires the plurality of history information stored in the storage unit 114.
The classification unit 131 classifies a plurality of pieces of history information and outputs classification results. The calculation section 132 calculates a calculation result (statistical value) using the classification result. The output unit 133 outputs the classification result and the calculation result. The display device 201 displays the classification result and the calculation result output by the output section 133.
The display example of the display device 201 in fig. 1 is a display example of the classification result described later using fig. 5, and a display example of the calculation result described later using fig. 7. In the present display example, a result obtained by calculating a statistical value on the basis of classifying a plurality of history information into 2 groups is shown.
As shown in fig. 2, the processing apparatus 100 has a CPU (Central Processing Unit: central processing unit) 111, a ROM (Read Only Memory) 112, a RAM (Random Access Memory: random access Memory) 113, a storage section 114, and an I/O interface 120. Which are connected via a bus in a manner that they are capable of communicating with each other.
The CPU111 uniformly controls the entire processing apparatus 100. The CPU111 loads the program stored in the ROM112 into the RAM113 and executes the program. The ROM112 stores a program in which processing steps of processing performed by the processing apparatus 100 are recorded.
The RAM113 serves as a work area when the CPU111 executes a program, and temporarily stores the program, data when the program is executed, and the like. The storage unit 114 is a nonvolatile storage device, and is, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive) or the like.
The I/O interface 120 is an interface for connecting the CPU111 with the display device 201 or the input device 202. The processing device 100 is connected to a display device 201 and an input device 202.
The display device 201 is, for example, a display. The display device 201 displays the result output by the output unit 133. The input device 202 is, for example, a keyboard or a mouse. For example, by the operation of the input device 202, it is possible to cause the processing device 100 to execute sorting processing of history information or the like, or to select content to be displayed by the display device 201.
The processing device 100 stores job history information (history information) of the building equipment 10 (10 a to 10c, etc.) such as an elevator in the storage unit 114.
Details of history information and handling time will be described below. Fig. 3 is a graph for explaining distribution of response times. In fig. 3, the vertical axis represents the handling time, and the horizontal axis represents the work date and time.
The response time may include a non-job time that is not related to the job, which is also referred to as "waiting time"). For example, the non-job time includes at least 1 of a non-job time a, a non-job time B, and a non-job time C.
The non-job time a is a time when the job is interrupted in order to prepare the components necessary for the job. For example, assume a case where the base needs to be replaced and prepared when the base of the elevator fails. The non-working time B is a time from when the maintenance person arrives at the building 1 to when the maintenance person enters the building 1. The non-work time C is a time when the work is interrupted to confirm the owner of the building 1 when the paid component is replaced. In the case where the parts to be replaced are paid, confirmation to the owner is required. In order to perform this confirmation, the building may have to be accessed again.
In the graph of fig. 3, as the non-job time, data including the non-job time a (data having "wait for occurrence" in the figure) and data not including the non-job time a (data having "wait for no occurrence" in the figure) are plotted.
As described above, in the case of preparing a component, maintenance personnel often prepare to temporarily lift the building 1, and perform work on the other hand when the component is obtained. In this case, even if the work is normally completed within 1 to 2 hours, the work may take days until the work is completed because the work spans days for preparation.
In this way, when data including the non-operation time a and data not including the non-operation time a, which are different in nature, exist in a mixed manner, it is difficult to grasp the time required for the actual operation even if the average time is obtained as a statistical value.
Then, in embodiment 1, the processing device 100 performs statistical processing on the data having such different properties, which is classified. The processing performed by the processing device 100 in embodiment 1 and the contents displayed on the display device 201 will be specifically described below with reference to fig. 4 to 8.
Fig. 4 is a flowchart of a process performed by the processing apparatus 100 according to embodiment 1. Fig. 5 is a diagram showing a display example of the classification result of embodiment 1. Fig. 6 to 8 are diagrams showing display examples of the calculation results of embodiment 1.
The processing performed by the processing apparatus 100 may also start processing by an operation (based on an operation of the input apparatus 202) by a user using the processing apparatus 100, for example. Hereinafter, the "step" will also be referred to as "S" only.
As shown in fig. 4, when the processing performed by the processing apparatus 100 starts, in S1, the acquisition unit 130 of the processing apparatus 100 acquires a plurality of pieces of history information stored in the storage unit 114, and advances the processing to S2.
In S2, the classification unit 131 of the processing apparatus 100 classifies the plurality of history information into a plurality of groups by the clustering method, and advances the process to S3. Specifically, the plurality of groups includes a 1 st group (no occurrence wait) containing no non-job time and a 2 nd group (occurrence wait) containing no job time. The classification section 131 classifies the plurality of history information into at least the 1 st group and the 2 nd group based on the coping time.
In S2, the classification unit 131 clusters data according to the nature of the data (such as the aggregation of the data). In this embodiment, clustering was performed using a Gaussian mixture model (GMM: gaussian Mixture Model). Thus, a plurality of gaussian distribution models (2 sets of 1 st and 2 nd in the example of fig. 5 described later) can be obtained. In addition, not limited to this, SOM (self-organizing map), hierarchical clustering, or the like may be used as a method of clustering.
In S3, the classifying unit 131 of the processing device 100 calculates the boundary time indicating the boundary between the 1 st group and the 2 nd group based on the classified 1 st group and 2 nd group, and advances the process to S4.
In S4, the output unit 133 of the processing apparatus 100 outputs the classification result of the classification unit 131, and the processing proceeds to S5. Thereby, the display device 201 displays the outputted classification result.
For example, as shown in fig. 5, the display device 201 displays a graph in which the response time is plotted on the horizontal axis and the frequency is plotted on the vertical axis. As shown in the figure, the graph is displayed by clustering in a state classified into a 1 st group (no occurrence wait) having a peak at a position where the response time is short and a 2 nd group (occurrence wait) having a peak at a position where the response time is longer than the 1 st group.
In the figure, it is seen that the plotted (square mark) on the horizontal axis shows that the frequency of occurrence becomes higher in the vicinity of the peak of group 1 and the vicinity of the peak of group 2. In addition, on the graph, the boundary time is also shown. In the present embodiment, a time (time for which the magnitude of the probability is exchanged) in which the frequency of the 1 st group is equal to the frequency of the 2 nd group is defined as the boundary time.
In S5, the calculation unit 132 of the processing apparatus 100 calculates a statistic value related to the response time for each of the plurality of groups classified by the classification unit 131, and advances the process to S6. The statistical value includes an average value of the response times of the 1 st group and an average value of the response times of the 2 nd group.
In S6, the calculation unit 132 of the processing apparatus 100 predicts the current or future statistic value from the time-series information of the statistic value, and advances the process to S7.
In S7, the output unit 133 of the processing apparatus 100 outputs the calculation result of the calculation unit 132, and the processing ends. The output unit 133 outputs time-series information of the statistic value.
For example, as shown in fig. 6, the display device 201 displays a graph in which the date and time of the work are plotted on the horizontal axis and the response time is plotted on the vertical axis. In the graph, the coping time (circle mark) of the 1 st group is plotted, and the passage of the average time X of the 1 st group with the passage of time is shown. Further, in the graph, the coping time (quadrangular marker) of the group 2 is plotted, and the passage of the average time Z of the group 2 with time is shown.
For example, according to the graph of fig. 6, it is possible to analyze each situation such that the average time X during which waiting occurs is not changed, but the average time X during which waiting does not occur is gradually improved, etc. although the component preparation and the like are performed.
Here, regarding the average value obtained in S5, for example, the average time may be obtained by dividing the group 1 and the group 2 into a month unit or a year unit, or the average value of all the data may be obtained.
Further, as the statistical value predicted from the time series information in S6, a predicted value of the average time X, that is, a predicted time XP is displayed. The latest average time X may be set as the predicted time XP, or the current or future predicted time XP may be estimated by a method such as a least square method based on the transition of the average time X.
Further, the statistical value information shown in fig. 7 can be displayed. In the pie chart, a probability (75%) of a response time of 1 hour or less, a probability (12.5%) of a response time of 1 to 3 hours, and a probability (12.5%) of a response time of 3 hours or more are shown as the pie chart, respectively.
In this example, the boundary time=3 hours is calculated. Therefore, the probability of the response time being 3 hours or more=the probability of the occurrence of the non-job time due to the component preparation (also referred to as "occurrence waiting probability").
Further, it is shown that the average of the response times when no waiting occurs (group 1) is X hours, the probability of occurrence of waiting is Y%, and the average of the response times when waiting occurs (group 2) is Z hours. The statistics shown above are calculated in S5.
As shown in fig. 8, a graph showing the time transition of the boundary time may be displayed. The display device 201 displays a graph in which the work date and time are plotted on the horizontal axis and the response time is plotted on the vertical axis.
In the figure, the response time of group 1 (circle mark) and the response time of group 2 (quadrangle mark) are plotted. Furthermore, the boundary times are shown at their boundaries. In this example, the maintenance method is changed at the illustrated timing. For example, the maintenance method is changed by improving a maintenance manual or re-modifying the component preparation method.
In the figure, when the timing of the maintenance method is changed, the boundary time decreases. This makes it possible to grasp the effect of improving the response time by changing the maintenance method.
As described above, by classifying the plurality of pieces of history information into at least the 1 st group including no non-operation time and the 2 nd group including no operation time based on the response time, even when the time including no operation time is different in nature, the analysis related to the response time can be appropriately performed. Further, by calculating and outputting the statistics related to the response time for each of the plurality of groups classified, the statistics related to the response time can be performed on the basis of excluding the non-job time related to the job. Further, it is also possible to predict the average value of the response time in this case (prediction time XP in fig. 6) and grasp the improvement effect by the change of the maintenance method (fig. 8).
[ embodiment 2 ]
Fig. 9 is a diagram showing an example of a functional block diagram of the processing device 100 according to embodiment 2. Fig. 10 is a flowchart of a process performed by the processing device 100 according to embodiment 2.
In embodiment 1, the processing apparatus 100 includes a storage unit 114, an acquisition unit 130, a classification unit 131, a calculation unit 132, and an output unit 133. In embodiment 2, the processing apparatus 100 further includes a dividing unit 140 and a determining unit 141. Embodiment 2 will be described below with reference to fig. 9 and 10. In the description of embodiment 2, the differences from embodiment 1 will be described, and the description of common parts will be omitted.
As shown in fig. 10, when the processing performed by the processing apparatus 100 is started, in S11, the acquisition unit 130 of the processing apparatus 100 acquires a plurality of pieces of history information stored in the storage unit 114, and advances the processing to S12. The process is the same as S1.
Here, the plurality of pieces of history information include a plurality of pieces of related information related to the building 1 (1 a to 1c, etc.) or the building equipment 10 (10 a to 10c, etc.), in addition to the response time, respectively. The plurality of pieces of related information include at least 1 of the type of building 1 (the type of building), the number of years of operation of the elevator, the type of elevator (model), the type of elevator failure, the type of maintenance contract, and the skill of maintenance personnel.
The kinds of buildings are classified into commercial buildings, office buildings, and the like, for example. The type of elevator failure (failure type) is classified into, for example, a door-related failure, a brake-related failure, a failure of the control device, and the like. The type of maintenance contract includes, for example, a contract (hereinafter referred to as "a contract") for replacing a predetermined component, a contract (hereinafter referred to as "B contract") for replacing the predetermined component, and the like. The skills of maintenance personnel may be classified into, for example, those having experience years of 3 years or less, 3 to 7 years, and 7 years or more as maintenance personnel.
In S12, the determining unit 141 of the processing apparatus 100 determines information for dividing the plurality of pieces of history information by the dividing unit 140 from the plurality of pieces of related information, and advances the process to S13. For example, the determination unit 141 determines to divide the history information for each model of the elevator. Or may also decide to divide the history information per contract. For example, when there are model a, model B, contract a, contract B, 4 types of model a, contract a, model a, contract B, model B, contract a, model B, and contract B are divided.
The determination unit 141 may divide the image based on the division information specified by the user. The determination unit 141 may determine whether or not to perform division based on the division information specified by the user using a test or the like. In the case of determining using the test, for example, the t test, the Mann-Whitney U test, or the like may be used. When the amount of data is small, data having different properties (whether waiting occurs or not) may not be classified smoothly. In this case, the above-described divided data is taken as a measure for synthesizing again.
In S13, the dividing unit 140 of the processing apparatus 100 divides the plurality of pieces of history information based on at least 1 piece of the plurality of pieces of related information, and advances the process to S14. Specifically, the plurality of history information is divided based on the information determined in S12.
In S14, the classification unit 131 of the processing apparatus 100 classifies each of the plurality of pieces of history information divided by the division unit 140 into a plurality of groups, and advances the process to S15.
In S15, the classifying unit 131 of the processing device 100 calculates the boundary time indicating the boundary between the 1 st group and the 2 nd group based on the classified 1 st group and 2 nd group, and advances the process to S16. The process is the same as S3.
In S16, the output unit 133 of the processing apparatus 100 outputs the classification result of the classification unit 131, and the processing proceeds to S17. The process is the same as S4. Thus, for example, a graph showing the frequency of the 1 st group and the 2 nd group shown in fig. 5 may be shown for each piece of divided history information. For example, the above-described chart may be displayed for each type of maintenance contract.
In S17, the determining unit 141 of the processing apparatus 100 determines information for dividing the plurality of pieces of history information by the dividing unit 140 from the plurality of pieces of related information, and advances the process to S18. The determination unit 141 performs division based on the division information designated by the user. This may be the same as or different from the determination by the determining unit 141 in S12. For example, in S12, the history information may be divided for each model of the elevator, and in S17, the history information may be divided for each model of the elevator and for each contract (divided into 4 pieces described above). In both S12 and S17, the history information may be divided for each model of the elevator and for each contract.
In S18, the dividing unit 140 of the processing apparatus 100 also divides each of the plurality of groups classified by the classifying unit 131 based on at least 1 of the plurality of pieces of association information, and advances the process to S19. Specifically, each of the plurality of groups classified by the classification section 131 is divided based on the information determined in S17.
In S19, the calculation unit 132 of the processing apparatus 100 calculates a statistic value for each of the plurality of groups divided by the division unit 140 and classified by the classification unit 131, and advances the process to S20.
In S20, the output unit 133 of the processing apparatus 100 outputs the calculation result of the calculation unit 132, and the processing ends. The output unit 133 outputs time-series information of the statistic value. The process is the same as S7.
For example, as shown in fig. 9, the same pie chart as in the upper part of fig. 7 may be displayed. In this example, the history data is divided for each type of maintenance contract, and a pie chart is displayed for each type of maintenance contract.
As described above, the "a contract" is a contract for paid replacement of a predetermined part, and the "B contract" is a contract for paid replacement. In contract a, the probability of the response time being 1 hour or less is 75%, the probability of the response time being 1 to 3 hours is 12.5%, and the probability of the response time being 3 hours or more is 12.5%.
On the other hand, in contract B, the probability of the response time being 1 hour or less is 85%, the probability of the response time being 1 to 3 hours is 10%, and the probability of the response time being 3 hours or more is 5%. As can be seen from the graph, the response time of the B contract is shorter than that of the a contract.
When replacement of parts is paid, it is necessary to confirm the maintenance contract owner, and a waiting time is generated due to the confirmation. Therefore, in the case of the contract B that can be replaced without compensation, the waiting time for confirmation is not generated, and the response time is shortened accordingly, compared with the contract a. For example, when a maintenance contract is proposed, a use method is proposed in which a graph for comparing an a contract with a B contract is presented and a B contract having a short response time is recommended.
The information may be displayed as shown in the lower part of fig. 7. In this example, the history data is divided for each failure category, and the average handling time and the like are displayed in the failure category.
The case where the failure category is failure category C shows: the average of the response times when no waiting occurs (group 1) is X1 hours, the probability of occurrence of non-job time due to component preparation (occurrence waiting probability) is Y1%, and the average of the response times when waiting occurs (group 2) is Z1 hour.
The case where the failure category is failure category D shows: the average of the response times when no waiting occurs (group 1) is X2 hours, the probability of occurrence of non-job time due to component preparation (occurrence waiting probability) is Y2%, and the average of the response times when waiting occurs (group 2) is Z2 hours.
For example, in the case of a door-related problem (failure), it is mostly only a slight failure that is eliminated on the spot where an object is caught by the door. In the light failure, there are failure that is eliminated if the trash is removed, contact failure of the connector, and the like. In contrast, for example, there is also a failure that takes time before the completion of the work, because of the need for adjustment, replacement of components, and the like, due to a failure of the brake or the substrate. In this way, the analysis and prediction of the response time can be performed for each failure type having different properties.
Regarding the kind of building (building), for example, if it is a commercial building, there is a limit in access time within a business period, or there are cases where access can be made for 24 hours or not outside the time, depending on the building. Thus, the nature of the data varies depending on the kind of building.
Regarding the number of operation years of the elevator, the longer the number of operation years, the greater the effect of aging on the failure. In the case of the old model, there are differences in the model of the elevator such as the inventory of no parts, the time taken for preparation, and the like. Further, it is also assumed that the coping time varies according to the skill (years of experience) of the maintenance person.
As described above, the analysis related to the response time can be appropriately performed according to the plurality of pieces of related information related to the building equipment 10, respectively, in addition to excluding the non-job time that is not related to the job.
For example, the classification by the classifying unit 131 shown in fig. 9 and 10 may be performed in advance. When a problem occurs in the elevator, the control device of the elevator may report the type of the fault together with the model information of the elevator, and the processing device 100 may be configured to be able to receive the report information. Upon receiving the report information, the processing device 100 causes the calculation unit 132 to calculate the statistics of the types of failures in the reported model. By checking the average handling time and the like calculated by the calculating unit 132, the maintenance person can quickly grasp a predicted value of the handling time required for handling the problem, a probability of waiting time due to component preparation and the like, and the like.
When an elevator is defective, it is required to shorten the time for stopping the elevator due to the defective condition (shorten the response time) as much as possible. With the above-described configuration, if the response time becomes long due to component preparation, for example, the response time can be shortened by preparing the component in advance. As for analysis related to the response time, the more history information accumulated in the past, the more pieces of related information related to the building equipment 10 can be analyzed more closely and accurately. As described above, in the present embodiment, the response time can be shortened while effectively using the stored past history information.
[ Main Structure and Effect ]
Hereinafter, the main configuration and effects of the above-described embodiment will be described.
(1) The processing device 100 processes information related to maintenance of building devices 10 (10 a to 10c, etc.) provided in the building 1 (1 a to 1c, etc.). The processing apparatus 100 includes an acquisition unit 130, a classification unit 131, and an output unit 133. The acquisition unit 130 acquires a plurality of pieces of history information. The classification section 131 classifies the plurality of history information into a plurality of groups by a clustering method. The output unit 133 outputs the classification result of the classification unit 131. The plurality of history information includes a response time from when a maintenance person arrives at the building 1 to when the job related to the building equipment 10 is completed. The plurality of groups includes a 1 st group including no non-job time and a 2 nd group including no job time. The classification section 131 classifies the plurality of history information into at least the 1 st group and the 2 nd group based on the coping time. In this way, by classifying the plurality of pieces of history information into at least the 1 st group including no-operation time and the 2 nd group including no-operation time based on the response time, it is possible to appropriately perform analysis related to the response time even when the times including the non-operation time are different in nature.
(2) The classifying unit 131 calculates the boundary time indicating the boundary between the 1 st group and the 2 nd group based on the classified 1 st group and 2 nd group. This makes it possible to grasp the time at which the group 1 and the group 2 are bordered.
(3) The non-working time includes at least 1 of a time from when a maintenance person arrives at the building 1 (1 a to 1c, etc.) to when the maintenance person enters the building 1 (1 a to 1c, etc.), a time when the work is interrupted for preparing components necessary for the work, and a time when the work is interrupted for obtaining confirmation of the owner of the building 1 when the paid component is replaced. This makes it possible to analyze the response time while excluding the time for preparing the component, etc., which is not related to the work.
(4) The processing device 100 further includes a calculation unit 132. The calculation unit 132 calculates statistics related to the response time for each of the plurality of groups classified by the classification unit 131. The output unit 133 further outputs the calculation result of the calculation unit 132. Thus, statistics on the response time can be performed while excluding the non-work time associated with the work.
(5) The statistical value includes an average value of the response times of the 1 st group and an average value of the response times of the 2 nd group. This makes it possible to grasp the average value of the response time, excluding the non-work time associated with the work.
(6) The output unit 133 outputs time-series information of the statistic value. This makes it possible to grasp the time lapse of the statistical value, excluding the non-work time associated with the work.
(7) The calculation unit 132 predicts a current or future statistic value from time-series information of the statistic value. The current or future statistics can be predicted on the basis of excluding non-job time that is not related to the job.
(8) The plurality of history information includes a plurality of pieces of related information related to the building 1 (1 a to 1c, etc.) or the building equipment 10 (10 a to 10c, etc.), in addition to the response time, respectively. The processing apparatus 100 further includes a dividing unit 140. The dividing section 140 divides the plurality of history information based on at least 1 of the plurality of associated information. The classification unit 131 classifies each of the plurality of pieces of history information divided by the division unit 140 into a plurality of groups. Thus, analysis related to the response time can be appropriately performed according to each of the plurality of pieces of related information related to the building equipment 10.
(9) The dividing section 140 also divides each of the plurality of groups classified by the classifying section 131 based on at least 1 of the plurality of pieces of association information. The calculation unit 132 calculates a statistic value for each of the plurality of groups divided by the division unit 140 and classified by the classification unit 131. Thus, the statistics related to the handling time can be grasped for each of the plurality of pieces of related information related to the building equipment 10.
(10) The plurality of pieces of related information include at least 1 of the type of the building 1 (1 a to 1c, etc.), the type of the elevator failure, and the type of the maintenance contract. Thus, statistics related to the handling time can be grasped for each type of building 1, each type of elevator failure, and each type of maintenance contract.
(11) The processing apparatus 100 further includes a determining unit 141. The determination unit 141 determines information for dividing the plurality of pieces of history information by the dividing unit 140 from the plurality of pieces of related information. In the case where information for dividing the plurality of pieces of history information is decided by the examination, it is not necessary to conduct a study on how the plurality of pieces of history information are divided appropriately.
(12) The processing method is a method of processing information related to maintenance of the building devices 10 (10 a to 10c, etc.) installed in the building 1 (1 a to 1c, etc.). The processing method includes a step of acquiring a plurality of pieces of history information, a step of classifying the plurality of pieces of history information into a plurality of groups by a clustering method, and a step of outputting a classification result of the classifying step. The plurality of history information includes a response time from when a maintenance person arrives at the building 1 to when the job related to the building equipment 10 is completed. The plurality of groups includes a 1 st group including no non-job time and a 2 nd group including no job time. In the classifying step, the plurality of history information is classified into at least the 1 st group and the 2 nd group based on the coping time. In this way, by classifying the plurality of pieces of history information into at least the 1 st group including no-operation time and the 2 nd group including no-operation time based on the response time, it is possible to appropriately perform analysis related to the response time even when the times including the non-operation time are different in nature.
The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The scope of the present disclosure is shown not by the above description but by the claims, and includes all modifications within the meaning and scope equivalent to the claims.
Description of the reference numerals
1. 1 a-1 c building, 10 a-10 c building equipment, 100 processing device, 111CPU,112ROM,113RAM,114 storage unit, 120I/O interface, 201 display device, 202 input device, 130 acquisition unit, 131 classification unit, 133 output unit, 132 calculation unit, 140 division unit, 141 determination unit.
Claims (12)
1. A processing apparatus that processes information related to maintenance of building equipment provided in a building, wherein,
the processing device is provided with:
an acquisition unit that acquires a plurality of pieces of history information;
a classification unit that classifies the plurality of history information into a plurality of groups by a clustering method; and
an output unit that outputs a classification result of the classification unit,
the plurality of history information includes a response time from when a maintenance person arrives at the building to when a job related to the building equipment is completed,
the plurality of groups includes a 1 st group including no non-working time and a 2 nd group including the non-working time,
the classifying section classifies the plurality of history information into at least the 1 st group and the 2 nd group based on the coping time.
2. The processing apparatus according to claim 1, wherein,
the classifying unit calculates a boundary time indicating a boundary between the 1 st group and the 2 nd group based on the classified 1 st group and 2 nd group.
3. The processing apparatus according to claim 1 or 2, wherein,
the non-working time includes at least 1 of a time from when the maintenance person arrives at the building to when the building is entered, a time when the work is interrupted in order to prepare a part required for the work, and a time when the work is interrupted in order to obtain confirmation of an owner of the building when a paid part is replaced.
4. A processing apparatus according to any one of claims 1 to 3, wherein,
the processing device further includes a calculation unit that calculates a statistic value related to the response time for each of the plurality of groups classified by the classification unit,
the output section further outputs the calculation result of the calculation section.
5. The processing apparatus according to claim 4, wherein,
the statistical value includes an average of the coping times of the 1 st group and an average of the coping times of the 2 nd group.
6. The processing apparatus according to claim 4 or 5, wherein,
the output unit outputs time-series information of the statistic.
7. The processing apparatus according to claim 6, wherein,
the calculation unit predicts the current or future statistic value from time-series information of the statistic value.
8. The processing apparatus according to any one of claims 4 to 7, wherein,
the plurality of history information includes a plurality of associated information associated with the building or the building device in addition to the corresponding time, respectively,
the processing device further includes a dividing section that divides the plurality of history information based on at least 1 of the plurality of associated information,
the classification unit classifies each of the plurality of pieces of history information divided by the division unit into the plurality of groups.
9. The processing apparatus according to claim 8, wherein,
the dividing section further divides each of the plurality of groups classified by the classifying section based on at least 1 of the plurality of pieces of association information,
the calculation section calculates the statistical value for each of the plurality of groups divided by the division section and classified by the classification section.
10. The processing apparatus according to claim 8 or 9, wherein,
the plurality of associated information includes at least 1 of a kind of the building, a kind of an elevator, a kind of a fault of the elevator, and a kind of the maintenance contract.
11. The processing apparatus according to any one of claims 8 to 10, wherein,
the processing device further includes a determination unit that determines information for dividing the plurality of pieces of history information by the dividing unit from the plurality of pieces of related information.
12. A processing method of processing information related to maintenance of building equipment provided in a building, wherein,
the processing method comprises the following steps:
acquiring a plurality of history information;
classifying the plurality of history information into a plurality of groups by a clustering method; and
outputting a classification result of the step of classifying,
the plurality of history information includes a response time from when a maintenance person arrives at the building to when a job related to the building equipment is completed,
the plurality of groups includes a 1 st group including no non-working time and a 2 nd group including the non-working time,
in the classifying, the plurality of history information is classified into at least the 1 st group and the 2 nd group based on the coping time.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/JP2021/022714 WO2022264273A1 (en) | 2021-06-15 | 2021-06-15 | Processing device and processing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117546189A true CN117546189A (en) | 2024-02-09 |
Family
ID=84526350
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202180099392.XA Pending CN117546189A (en) | 2021-06-15 | 2021-06-15 | Processing apparatus and processing method |
Country Status (3)
| Country | Link |
|---|---|
| JP (1) | JP7336582B2 (en) |
| CN (1) | CN117546189A (en) |
| WO (1) | WO2022264273A1 (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009151733A (en) * | 2007-12-25 | 2009-07-09 | Hitachi Ltd | Facility check schedule making support apparatus and method, and facilities management system |
| CN108537386A (en) * | 2018-04-13 | 2018-09-14 | 上海财经大学 | Maintenance forecast device based on history maintenance record |
| CN110503322A (en) * | 2019-08-13 | 2019-11-26 | 成都飞机工业（集团）有限责任公司 | A kind of Military Maintenance appraisal procedure |
| CN112307685A (en) * | 2020-10-29 | 2021-02-02 | 河海大学常州校区 | Fluid motion boundary prediction method based on Gaussian mixture model in underwater acoustic sensor network |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4045074B2 (en) * | 2001-01-31 | 2008-02-13 | 株式会社日立製作所 | Elevator maintenance contract support system |
| JP2004030088A (en) | 2002-06-25 | 2004-01-29 | Mitsubishi Electric Corp | Work period management system and recording medium used therefor |
| JP4266014B2 (en) | 2005-02-25 | 2009-05-20 | 富士通株式会社 | Business information management program |
| JP2013242774A (en) * | 2012-05-22 | 2013-12-05 | Mitsubishi Electric Building Techno Service Co Ltd | Maintenance work schedule preparation device and program |
| JP6467264B2 (en) | 2015-03-30 | 2019-02-06 | 株式会社日立製作所 | Plan creation support apparatus and plan creation support method |
| JP6655001B2 (en) | 2016-12-21 | 2020-02-26 | 株式会社日立ビルシステム | Failure support server and failure support method |
-
2021
- 2021-06-15 WO PCT/JP2021/022714 patent/WO2022264273A1/en active Application Filing
- 2021-06-15 CN CN202180099392.XA patent/CN117546189A/en active Pending
- 2021-06-15 JP JP2022502124A patent/JP7336582B2/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009151733A (en) * | 2007-12-25 | 2009-07-09 | Hitachi Ltd | Facility check schedule making support apparatus and method, and facilities management system |
| CN108537386A (en) * | 2018-04-13 | 2018-09-14 | 上海财经大学 | Maintenance forecast device based on history maintenance record |
| CN110503322A (en) * | 2019-08-13 | 2019-11-26 | 成都飞机工业（集团）有限责任公司 | A kind of Military Maintenance appraisal procedure |
| CN112307685A (en) * | 2020-10-29 | 2021-02-02 | 河海大学常州校区 | Fluid motion boundary prediction method based on Gaussian mixture model in underwater acoustic sensor network |
Non-Patent Citations (2)
| Title |
|---|
| 汪洪桥: "《Python机器学习算法及典型案例实战》", 31 January 2021, 西北工业大学出版社, pages: 92 - 94 * |
| 牛文旭著: "《燃料电池汽车动力系统分布式测试数据传输研究》", 31 March 2020, 机械工业出版社, pages: 106 * |
Also Published As
| Publication number | Publication date |
|---|---|
| JP7336582B2 (en) | 2023-08-31 |
| WO2022264273A1 (en) | 2022-12-22 |
| JPWO2022264273A1 (en) | 2022-12-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8087001B2 (en) | Computer-implemented systems and methods for software application testing | |
| US20060282189A1 (en) | Manufacturing control apparatus, manufacturing control method, and computer product | |
| JP2006522410A (en) | Statistical analysis and control of preventive maintenance procedures | |
| CN104364664B (en) | An algorithm and structure for creation, definition, and execution of an SPC rule decision tree | |
| CN104583789A (en) | Creation and scheduling of a decision and execution tree of a test cell controller | |
| CN104335056A (en) | Interposer between a tester and material handling equipment to separate and control different requests of multiple entities in a test cell operation | |
| CN112180230A (en) | Chip test parameter abnormity detection method, storage medium and terminal | |
| Ke et al. | Software reliability prediction and management: A multiple change‐point model approach | |
| CN111095424A (en) | Clinical trial support system, clinical trial support program, and clinical trial support method | |
| JP2003157309A (en) | Worker posting planning device and worker posting planning method | |
| JP6975086B2 (en) | Quality evaluation method and quality evaluation equipment | |
| CN116993286B (en) | Test management system and method based on test progress reverse project progress | |
| US11443265B2 (en) | Analysis system and analysis method | |
| CN117546189A (en) | Processing apparatus and processing method | |
| JPWO2019016892A1 (en) | Quality analyzer and quality analysis method | |
| US11921155B2 (en) | Dice testing method | |
| RU2295590C1 (en) | Method of the statistical control over the quality of the electrode products | |
| JP2008152350A (en) | Working time estimation device, working time estimation method, program and machine-readable recording medium | |
| JPH0876992A (en) | Device and method for evaluation and management of quality of software | |
| Zhang et al. | Mining software repositories for model-driven development | |
| US7584083B1 (en) | Modeling and simulation of workcenter processes | |
| Ware et al. | The use of product measures in tracking code development to completion within small to medium sized enterprises | |
| Yadav et al. | Testing effort-dependent software reliability growth model using time lag functions under distributed environment | |
| EP4092537A1 (en) | Computer-implemented method for updating a model parameter for a model | |
| Kanoun | A measurement-based framework for software reliability improvement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |